Magnetoencephalographic correlates of mood and reward dynamics in human adolescents

Abstract

Despite its omnipresence in everyday interactions and its importance for mental health, mood and its neuronal underpinnings are poorly understood. Computational models can help identify parameters affecting self-reported mood during mood induction tasks. Here, we test if computationally modeled dynamics of self-reported mood during monetary gambling can be used to identify trial-by-trial variations in neuronal activity. To this end, we shifted mood in healthy (N = 24) and depressed (N = 30) adolescents by delivering individually tailored reward prediction errors while recording magnetoencephalography (MEG) data. Following a pre-registered analysis, we hypothesize that the expectation component of mood would be predictive of beta-gamma oscillatory power (25–40 Hz). We also hypothesize that trial variations in the source localized responses to reward feedback would be predicted by mood and by its reward prediction error component. Through our multilevel statistical analysis, we found confirmatory evidence that beta-gamma power is positively related to reward expectation during mood shifts, with localized sources in the posterior cingulate cortex. We also confirmed reward prediction error to be predictive of trial-level variations in the response of the paracentral lobule. To our knowledge, this is the first study to harness computational models of mood to relate mood fluctuations to variations in neural oscillations with MEG.

Spatiotemporal Precision of Neuroimaging in Psychiatry

“…Neuroimaging can complement such computational models of decision-making in psychopathology by measuring a "reward prediction error" signal (i.e., the difference between the reward that was received and the reward that was expected), a key computational component in reinforcement learning and active inference models (106). Reward prediction error signals localise to specific neurochemical circuitry (e.g., dopaminergic pathways) and are observable in both M/EEG (107,108) and fMRI (109).…”

A cognitive-computational account of mood swings in adolescence

“…Only recently, studies have begun to expand the scope of RL to include the dynamics of mood [84]. These studies revealed that mood fluctuations are contingent not only on the valence of outcomes ('good vs. bad'), but also on how surprised individuals are by the outcome of their actions, i.e., by RPEs [13,16,19,85,86]. RPEs can indeed elicit rapid mood changes, for example when one's soccer club loses unexpectedly [13,20,21].…”

“…Recently, computational approaches to mood have also partially been extended towards models that seek to explain mental health problems [85,86,135,136]. Indeed, many mental health problems that are characterized by elevated mood fluctuations like affective disorders, Attention Deficit/Hyperactivity Disorder and Borderline Personality Disorder show their typical onset in childhood and adolescence [7][8][9].…”

On the misery of cognitive effort

“…On the other hand, while mood states are responsive to appetitive and aversive events (Diener et al, 2009;Grosscup & Lewinsohn, 1980;Philip, 1971;Stone & Neale, 1984), it is unknown how cognitive effort influences mood at the moment of expenditure. There is strong evidence for the influence of appetitive events on mood, and current evidence suggests that momentary mood reflects the statistics of recent rewards (Keren et al, 2021;Liuzzi et al, 2021). Considering that cognitive effort is both aversive and closely linked to reward, one could speculate that cognitive effort itself should have an impact on mood.…”